Towards Automating the Identification of Sustainable Projects Seeking Financial Support: An AI-Powered Approach

Hojat Behrooz, Carlo Lipizzi, George Korfiatis, Mohammad Ilbeigi (), Martin Powell and Mina Nouri
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Hojat Behrooz: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030, USA
Carlo Lipizzi: School of Systems and Enterprises, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030, USA
George Korfiatis: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030, USA
Mohammad Ilbeigi: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030, USA
Martin Powell: Siemens Financial Services, Iselin, NJ 08830, USA
Mina Nouri: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030, USA

Sustainability, 2023, vol. 15, issue 12, 1-12

Abstract: The criticality of sustainable development to control the unprecedented consequences of climate change is clear. A vital element in launching sustainability projects is financing, especially for projects by small and medium enterprises. The first and crucial step to offering financing services for sustainable development is to identify and evaluate promising projects. The current practice to accomplish this step heavily depends on subject-matter expertise and professional networks. The current practice also involves extensive manual document reviews and subjective decisions. Therefore, existing methods are time-consuming, inefficient, and not scalable. This study proposes an automated system to identify potential sustainability projects for financing services using Artificial Intelligence (AI). The proposed method uses web crawlers and text mining solutions, including Natural Language Processing (NLP), to search the Internet, analyze text data, evaluate the information quantitatively, and identify potential sustainability projects for financing services. The proposed method was implemented and empirically assessed. The results indicate that the AI-enhanced system is able to identify and prioritize potential sustainability projects with 87% accuracy. The outcomes of this study will help financial experts and decision-makers take advantage of the information available on the Internet efficiently to improve the existing methods for identifying potential projects for financing services.

Keywords: sustainability projects; financing; artificial intelligence; natural language processing; web crawler (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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